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Flash-based SSDs have become well established in the storage market, replacing magnetic disks in both enterprise and consumer computer systems. The performance characteristics of these new devices have prompted a considerable amount of research that aims at developing efficient data access methods. Early works attempted to reduce the expensive random writes, exploiting logging and batch write techniques, while more recent ones addressed query processing, taking advantage of the high internal parallelism of SSDs. 3D XPoint is a new nonvolatile memory technology that has emerged recently, featuring smaller access times and higher durability compared with flash. It is available both as block addressable secondary storage and as byte addressable persistent main memory. However, the high cost of 3D XPoint prevents, for the moment, its adoption in large scales. This renders hybrid storage systems utilizing NAND flash and 3D XPoint a sufficient alternative. In this work, we propose HyR-tree, a hybrid variant of R-tree that persists a part of the tree in the high performing 3D XPoint storage. HyR-tree identifies repeated access pattern to the data and uses these patterns to locate the most important nodes. The importance of a node is determined by the performance gain that derives from its placement within a 3D XPoint-based device. We experimentally evaluated HyR-tree using real devices and four different datasets. The acquired results show that our proposal achieves significant performance gains up to 40% in tree construction and up to 56% in range queries.
Athanasios Fevgas; Leonidas Akritidis; Miltiadis Alamaniotis; Panagiota Tsompanopoulou; Panayiotis Bozanis. HyR-tree: a spatial index for hybrid flash/3D XPoint storage. Neural Computing and Applications 2021, 1 -13.
AMA StyleAthanasios Fevgas, Leonidas Akritidis, Miltiadis Alamaniotis, Panagiota Tsompanopoulou, Panayiotis Bozanis. HyR-tree: a spatial index for hybrid flash/3D XPoint storage. Neural Computing and Applications. 2021; ():1-13.
Chicago/Turabian StyleAthanasios Fevgas; Leonidas Akritidis; Miltiadis Alamaniotis; Panagiota Tsompanopoulou; Panayiotis Bozanis. 2021. "HyR-tree: a spatial index for hybrid flash/3D XPoint storage." Neural Computing and Applications , no. : 1-13.
Modern cyber-physical systems have become more autonomous and distributed with the inclusion of advanced control architectures and communication networks. Power electronics-based inverters that employ extensive communication structures are integral part of such systems. The controllers for inverter-based systems rely on communication networks that make them vulnerable to cyber-physical anomalies. The cyber anomalies occur due to malicious attacks targeting the communication layer and physical anomalies are caused by power system faults in the physical layer of the microgrid. In this work, an intelligent anomaly identification (IAI) technique for such systems is presented utilizing data driven artificial intelligence tools that employ multi class support vector machines (MSVM) for anomaly classification and localization. The effects of cyber anomalies such as false data injection and denial of service attacks that target the communication network are considered in this work. In addition, the physical anomalies due to power system faults are also considered. The proposed technique utilizes statistical features extracted from measurements for optimal learning of a dual of MSVM classifiers. The mean absolute percentage error is used as a performance metric and the results are validated by comparing to artificial neural network, Naive Bayes classification and using real time simulations in OPAL-RT.
Asad Ali Khan; Omar Ali Beg; Miltiadis Alamaniotis; Sara Ahmed. Intelligent anomaly identification in cyber-physical inverter-based systems. Electric Power Systems Research 2021, 193, 107024 .
AMA StyleAsad Ali Khan, Omar Ali Beg, Miltiadis Alamaniotis, Sara Ahmed. Intelligent anomaly identification in cyber-physical inverter-based systems. Electric Power Systems Research. 2021; 193 ():107024.
Chicago/Turabian StyleAsad Ali Khan; Omar Ali Beg; Miltiadis Alamaniotis; Sara Ahmed. 2021. "Intelligent anomaly identification in cyber-physical inverter-based systems." Electric Power Systems Research 193, no. : 107024.
In the smart cities of the future artificial intelligence (AI) will have a dominant role given that AI will accommodate the utilization of intelligent analytics for prediction of critical parameters pertaining to city operation. In this chapter, a new data analytics paradigm is presented and being applied for energy demand forecasting in smart cities. In particular, the presented paradigm integrates a group of kernel machines by utilizing a deep architecture. The goal of the deep architecture is to exploit the strong capabilities of deep learning utilizing various abstraction levels and subsequently identify patterns of interest in the data. In particular, a deep feedforward deep neural network is employed with every network node to implement a kernel machine. This deep architecture, named neuro-kernel machine network, is subsequently applied for predicting the energy consumption of groups of residents in smart cities. Obtained results exhibit the capability of the presented method to provide adequately accurate predictions despite the form of the energy consumption data.
Miltiadis Alamaniotis. Neuro-Kernel-Machine Network Utilizing Deep Learning and Its Application in Predictive Analytics in Smart City Energy Consumption. Springer Texts in Business and Economics 2020, 293 -307.
AMA StyleMiltiadis Alamaniotis. Neuro-Kernel-Machine Network Utilizing Deep Learning and Its Application in Predictive Analytics in Smart City Energy Consumption. Springer Texts in Business and Economics. 2020; ():293-307.
Chicago/Turabian StyleMiltiadis Alamaniotis. 2020. "Neuro-Kernel-Machine Network Utilizing Deep Learning and Its Application in Predictive Analytics in Smart City Energy Consumption." Springer Texts in Business and Economics , no. : 293-307.
This chapter frames in the realm of Internet of Things (IoT) and provides a new deep learning solution for securing networks connecting IoT devices. In particular, it discusses a comprehensive solution to enhancing IoT defense in the form of a new protocol for IoT security. The need for IoT security solutions was revisited after the recent attacks on 120 million devices. In the current work, deep learning is studied for critical security applications by utilizing snapshots of network traffic taken from a set of nine real-world IoT devices. To that end, a set of learning tools such as Support Vector Machines (SVM), Random Forest and Deep Neural Network (DNN) are tested on a set of real-world data to detect anomalies in the IoT networks. Obtained results provided high accuracy for all tested algorithms. Notably, DNN exhibits the highest coefficient of determination among the tested models, thus, promoting the DNN as a more suitable solution in IoT security applications. Furthermore, the DNN’s learning autonomy feature results in a time efficient real-world algorithm because it skips human intervention.
Luke Holbrook; Miltiadis Alamaniotis. A Good Defense Is a Strong DNN: Defending the IoT with Deep Neural Networks. Learning and Analytics in Intelligent Systems 2020, 125 -145.
AMA StyleLuke Holbrook, Miltiadis Alamaniotis. A Good Defense Is a Strong DNN: Defending the IoT with Deep Neural Networks. Learning and Analytics in Intelligent Systems. 2020; ():125-145.
Chicago/Turabian StyleLuke Holbrook; Miltiadis Alamaniotis. 2020. "A Good Defense Is a Strong DNN: Defending the IoT with Deep Neural Networks." Learning and Analytics in Intelligent Systems , no. : 125-145.
In recent years, the interest in properly conditioning the indoor environment of historic buildings has increased significantly. However, maintaining a suitable environment for building and artwork preservation while keeping comfortable conditions for occupants is a very challenging and multi-layered job that might require a considerable increase in energy consumption. Most historic structures use traditional on/off heating, ventilation, and air conditioning (HVAC) system controllers with predetermined setpoints. However, these controllers neglect the building sensitivity to occupancy and relative humidity changes. Thus, sophisticated controllers are needed to enhance historic building performance to reduce electric energy consumption and increase sustainability while maintaining the building historic values. This study presents an electric cooling air controller based on a fuzzy inference system (FIS) model to, simultaneously, control air temperature and relative humidity, taking into account building occupancy patterns. The FIS numerically expresses variables via predetermined fuzzy sets and their correlation via 27 fuzzy rules. This intelligent model is compared to the typical thermostat on/off baseline control to evaluate conditions of cooling supply during cooling season. The comparative analysis shows a FIS controller enhancing building performance by improving thermal comfort and optimizing indoor environmental conditions for building and artwork preservation, while reducing the HVAC operation time by 5.7%.
Antonio Martinez-Molina; Miltiadis Alamaniotis. Enhancing Historic Building Performance with the Use of Fuzzy Inference Systems to Control the Electric Cooling System. Sustainability 2020, 12, 5848 .
AMA StyleAntonio Martinez-Molina, Miltiadis Alamaniotis. Enhancing Historic Building Performance with the Use of Fuzzy Inference Systems to Control the Electric Cooling System. Sustainability. 2020; 12 (14):5848.
Chicago/Turabian StyleAntonio Martinez-Molina; Miltiadis Alamaniotis. 2020. "Enhancing Historic Building Performance with the Use of Fuzzy Inference Systems to Control the Electric Cooling System." Sustainability 12, no. 14: 5848.
Artificial intelligence is anticipated to play a significant role in the smart homes of the future. Decisions have to be made based on a variety of information that will be available to the home occupants. With regard to electricity consumption, it is expected that price directed markets will allow home occupants to become price receivers at a resolution of very short-term intervals of time—prices may be sent in intervals of a few seconds. In that time frame, decision patterns cannot be formed with the physical participation of the home occupants. To fill this gap, artificial intelligence offers the necessary tools to develop smart decision-making algorithms that make automated efficient decisions. In this chapter, a new approach for making decisions with regard to electricity consumption of smart appliances is presented. In particular, a neurofuzzy anticipatory approach—that integrates neural networks with fuzzy inference—is presented as a means to make decisions over the length of the operational time of a smart appliance. The goal of the approach is to utilize the current operational variables values and price information together with their future projections to make decisions over the operational time interval of a smart appliance. The determination of the operational time of each appliance, when aggregated implicitly shapes the demand response of the occupant in the price directed market. The proposed neurofuzzy approach is tested on a set of simulated data from an HVAC system obtained with the GridLAB-D simulation software, and real world price signals.
Miltiadis Alamaniotis; Iosif Papadakis Ktistakis. Neurofuzzy Approach for Control of Smart Appliances for Implementing Demand Response in Price Directed Electricity Utilization. Artificial Intelligence Techniques for a Scalable Energy Transition 2020, 261 -278.
AMA StyleMiltiadis Alamaniotis, Iosif Papadakis Ktistakis. Neurofuzzy Approach for Control of Smart Appliances for Implementing Demand Response in Price Directed Electricity Utilization. Artificial Intelligence Techniques for a Scalable Energy Transition. 2020; ():261-278.
Chicago/Turabian StyleMiltiadis Alamaniotis; Iosif Papadakis Ktistakis. 2020. "Neurofuzzy Approach for Control of Smart Appliances for Implementing Demand Response in Price Directed Electricity Utilization." Artificial Intelligence Techniques for a Scalable Energy Transition , no. : 261-278.
This paper frames itself in an informational rich smart electricity grid where consumers have access to various streams of information and make decisions over their daily consumption pattern. In particular, a new intelligent management system to accommodate possible optimal decisions for elastic load consumption is discussed. The energy management system implements a fuzzy driven leaky bucket that manages the elastic load of a consumer by controlling the token rate buffer via a set of four fuzzy variables (among them the electricity price). The goal of this innovative system is to allow loads that are identified as elastic to be scheduled only when it is potentially beneficial to the consumer. To that end, a fuzzy algorithm comprised of a set of rules is developed to manage the token rate of the leaky bucket and through that the decisions over the fate of elastic loads. The developed system is applied on a set of real-world electricity consumption data taken from a residential consumer, and benchmarked against a full scheduling method, where the elastic load is fully scheduled offline. Results exhibit that the proposed fuzzy logic method outperforms the full scheduling method in the vast majority of the cases, i.e., over 79% of the cases with respect to consumption cost. Furthermore, they validate its ability to conduct real time decision making with no human in the loop.
Miltiadis Alamaniotis. Fuzzy Leaky Bucket System for Intelligent Management of Consumer Electricity Elastic Load in Smart Grids. Frontiers in Artificial Intelligence 2020, 3, 1 .
AMA StyleMiltiadis Alamaniotis. Fuzzy Leaky Bucket System for Intelligent Management of Consumer Electricity Elastic Load in Smart Grids. Frontiers in Artificial Intelligence. 2020; 3 ():1.
Chicago/Turabian StyleMiltiadis Alamaniotis. 2020. "Fuzzy Leaky Bucket System for Intelligent Management of Consumer Electricity Elastic Load in Smart Grids." Frontiers in Artificial Intelligence 3, no. : 1.
Robust forecasting of wind speed values is a key element to effectively accommodate renewable generation from wind in smart power systems. However, the stochastic nature of wind and the uncertainties associated with it impose high challenge in its forecasting. A new method for forecasting wind speed in renewable energy generation is introduced in this study. The goal of the method is to provide a forecast in the form of an interval, which is determined by a mean value and the variance around the mean. In particular, the forecasting interval is produced according to a two-step process: in the first step, a set of individual kernel modelled Gaussian processes (GP) are utilised to provide a respective set of interval forecasts, i.e. mean and variance values, over the future values of the wind. In the second step, the individual forecasts are evaluated using a fuzzy driven multiplexer, which selects one of them. The final output of the methodology is a single interval that has been identified as the best among the GP models. The presented methodology is tested on the set of real-world data and benchmarked against the individual GPs as well as the autoregressive moving average model.
Miltiadis Alamaniotis; Georgios Karagiannis. Application of fuzzy multiplexing of learning Gaussian processes for the interval forecasting of wind speed. IET Renewable Power Generation 2019, 14, 100 -109.
AMA StyleMiltiadis Alamaniotis, Georgios Karagiannis. Application of fuzzy multiplexing of learning Gaussian processes for the interval forecasting of wind speed. IET Renewable Power Generation. 2019; 14 (1):100-109.
Chicago/Turabian StyleMiltiadis Alamaniotis; Georgios Karagiannis. 2019. "Application of fuzzy multiplexing of learning Gaussian processes for the interval forecasting of wind speed." IET Renewable Power Generation 14, no. 1: 100-109.
The future of electric power grid infrastructure is strongly associated with the heavy use of information and learning technologies. In this chapter, a new machine learning paradigm is presented focusing on the analysis of recorded electricity load data. The presented paradigm utilizes a set of multiple kernel functions to analyze a load signal into a set of components. Each component models a set of different data properties, while the coefficients of the analysis are obtained using an optimization algorithm and more specifically a simple genetic algorithm. The overall goal of the analysis is to identify the data properties underlying the observed loads. The identified properties will be used for building more efficient forecasting tools by retaining those kernels that are higher correlated with the observed signals. Thus, the multi-kernel analysis implements a “learning from loads” approach, which is a pure data driven method avoiding the explicit modeling of the factors that affect the load demand in smart power systems. The paradigm is applied on real world nodal load data taken from the Chicago metropolitan Area. Results indicate that the proposed paradigm can be used in applications where the analysis of load signals is needed.
Miltiadis Alamaniotis. Multi-kernel Analysis Paradigm Implementing the Learning from Loads Approach for Smart Power Systems. Learning and Analytics in Intelligent Systems 2019, 131 -148.
AMA StyleMiltiadis Alamaniotis. Multi-kernel Analysis Paradigm Implementing the Learning from Loads Approach for Smart Power Systems. Learning and Analytics in Intelligent Systems. 2019; ():131-148.
Chicago/Turabian StyleMiltiadis Alamaniotis. 2019. "Multi-kernel Analysis Paradigm Implementing the Learning from Loads Approach for Smart Power Systems." Learning and Analytics in Intelligent Systems , no. : 131-148.
Utilization of digital connectivity tools is the driving force behind the transformation of the power distribution system into a smart grid. This paper places itself in the smart grid domain where consumers exploit digital connectivity to form partitions within the grid. Every partition, which is independent but connected to the grid, has a set of goals associated with the consumption of electric energy. In this work, we consider that each partition aims at morphing the initial anticipated partition consumption in order to concurrently minimize the cost of consumption and ensure the privacy of its consumers. These goals are formulated as two objectives functions, i.e., a single objective for each goal, and subsequently determining a multi-objective problem. The solution to the problem is sought via an evolutionary algorithm, and more specifically, the non-dominated sorting genetic algorithm-II (NSGA-II). NSGA-II is able to locate an optimal solution by utilizing the Pareto optimality theory. The proposed load morphing methodology is tested on a set of real-world smart meter data put together to comprise partitions of various numbers of consumers. Results demonstrate the efficiency of the proposed morphing methodology as a mechanism to attain low cost and privacy for the overall grid partition.
Miltiadis Alamaniotis; Nikolaos Gatsis. Evolutionary Multi-Objective Cost and Privacy Driven Load Morphing in Smart Electricity Grid Partition. Energies 2019, 12, 2470 .
AMA StyleMiltiadis Alamaniotis, Nikolaos Gatsis. Evolutionary Multi-Objective Cost and Privacy Driven Load Morphing in Smart Electricity Grid Partition. Energies. 2019; 12 (13):2470.
Chicago/Turabian StyleMiltiadis Alamaniotis; Nikolaos Gatsis. 2019. "Evolutionary Multi-Objective Cost and Privacy Driven Load Morphing in Smart Electricity Grid Partition." Energies 12, no. 13: 2470.
The vision of smart cities has emerged from the integration of advances in information technologies to a city’s infrastructures and assets. The use of information will improve the daily operations, and lead to greener, safer, and more human friendly cities. The most preeminent city asset is the electrical power grid, upon which the modernization of human life is built. However, integration of power grid with information technologies, known as smart grid, comes at a cost of reduced privacy. Electricity consumer behavior expressed via daily consumption patterns may be visible to third parties, which can make critical inferences over the consumers’ personal life. In this paper, a new method is proposed that aims at enhancing consumer privacy in smart cities by proposing an intelligent aggregation of anticipated demand patterns of multiple consumers as a mean to hide individual features. To that end, the method utilizes consumers self-elasticities matrices and a genetic algorithm to create an aggregated pattern that masks individual consumption data. The proposed method is tested on real world electricity demand patterns, and morphing performance is recorded with respect to symmetric mean average percentage error (SMAPE) attaining morphing over 60% in all tested cases.
Miltiadis Alamaniotis; Nikolaos Bourbakis; Lefteri H. Tsoukalas. Enhancing privacy of electricity consumption in smart cities through morphing of anticipated demand pattern utilizing self-elasticity and genetic algorithms. Sustainable Cities and Society 2019, 46, 101426 .
AMA StyleMiltiadis Alamaniotis, Nikolaos Bourbakis, Lefteri H. Tsoukalas. Enhancing privacy of electricity consumption in smart cities through morphing of anticipated demand pattern utilizing self-elasticity and genetic algorithms. Sustainable Cities and Society. 2019; 46 ():101426.
Chicago/Turabian StyleMiltiadis Alamaniotis; Nikolaos Bourbakis; Lefteri H. Tsoukalas. 2019. "Enhancing privacy of electricity consumption in smart cities through morphing of anticipated demand pattern utilizing self-elasticity and genetic algorithms." Sustainable Cities and Society 46, no. : 101426.
A plethora of energy management opportunities has emerged for electricity consumers and producers by way of the transition from the current grid infrastructure to a smart grid. The aim of this chapter is to present a new dynamic data-driven applications systems (DDDAS) methodology for partitioning the smart distribution grid based on dynamically varying data. In particular, the proposed methodology uses the k-means algorithm for performing partitioning and a fuzzy decision making method for increasing power efficiency and reliability. The network is divided into a set of “similar” subnetworks; where the subnetworks are comprised of residential customers (i.e., residencies) who share the same characteristics pertaining to the energy needs but not necessarily the same geographic vicinity or belong to the same grid node. A fuzzy logic method is used to make decisions on which partitions could be offered energy at lower prices available from Renewable Energy Sources (RES). Various scenarios based on the GridLAB-D simulation platform exhibits how the operation of the smart grid is affected from the partition of the distribution grid. The illustrative example utilizes the IEEE-13, IEEE-37 and IEEE-123 bus test feeders in the experiments from a distribution grid composing 3004 residencies and both conventional and distributed generators.
Antonia Nasiakou; Miltiadis Alamaniotis; Lefteri H. Tsoukalas; Manolis Vavalis. Dynamic Data Driven Partitioning of Smart Grid Using Learning Methods. Handbook of Dynamic Data Driven Applications Systems 2018, 505 -526.
AMA StyleAntonia Nasiakou, Miltiadis Alamaniotis, Lefteri H. Tsoukalas, Manolis Vavalis. Dynamic Data Driven Partitioning of Smart Grid Using Learning Methods. Handbook of Dynamic Data Driven Applications Systems. 2018; ():505-526.
Chicago/Turabian StyleAntonia Nasiakou; Miltiadis Alamaniotis; Lefteri H. Tsoukalas; Manolis Vavalis. 2018. "Dynamic Data Driven Partitioning of Smart Grid Using Learning Methods." Handbook of Dynamic Data Driven Applications Systems , no. : 505-526.
Predictive monitoring supports the a priori scheduling of critical component maintenance and contributes significantly in attaining a safe yet economic operation and management of complex energy systems by mitigating the risk of accidents and minimizing the number of operational pauses. The current work studies the learning ability of probabilistic kernel machines, and more particularly of Gaussian Processes (GP) equipped with various kernels for the estimation of weld residual stress profiles of stainless steel pipe welds. The GP models are tested on experimentally-obtained data of axial and hoop residual stresses in two different stainless-steel pipes. The results exhibit the ability of GP to accurately predict the weld residual stress profile in the axial and hoop direction by providing a predictive distribution, i.e., mean and variance values. Furthermore, performance of GP is compared to a non-probabilistic kernel machine, such as support vector regression (SVR) equipped with the same kernels, and to multivariate linear regression (MLR). Comparison results exhibit the robustness of GP over SVR and MLR with respect to prediction accuracy of weld residual stress in terms of root mean square error. With respect to a second metric, namely, correlation coefficient between measured and predicted values, GP is superior to SVR and MLR in the majority of the cases.
Miltiadis Alamaniotis; Jino Mathew; Alexander Chroneos; Michael E. Fitzpatrick; Lefteri H. Tsoukalas. Probabilistic kernel machines for predictive monitoring of weld residual stress in energy systems. Engineering Applications of Artificial Intelligence 2018, 71, 138 -154.
AMA StyleMiltiadis Alamaniotis, Jino Mathew, Alexander Chroneos, Michael E. Fitzpatrick, Lefteri H. Tsoukalas. Probabilistic kernel machines for predictive monitoring of weld residual stress in energy systems. Engineering Applications of Artificial Intelligence. 2018; 71 ():138-154.
Chicago/Turabian StyleMiltiadis Alamaniotis; Jino Mathew; Alexander Chroneos; Michael E. Fitzpatrick; Lefteri H. Tsoukalas. 2018. "Probabilistic kernel machines for predictive monitoring of weld residual stress in energy systems." Engineering Applications of Artificial Intelligence 71, no. : 138-154.
The future of electric power is associated with the use of information technologies. The smart grid of the future will utilize communications and big data to regulate power flow, shape demand with a plethora of pieces of information and ensure reliability at all times. However, the extensive use of information technologies in the power system may also form a Trojan horse for cyberattacks. Smart power systems where information is utilized to predict load demand at the nodal level are of interest in this work. Control of power grid nodes may consist of an important tool in cyberattackers’ hands to bring chaos in the electric power system. An intelligent system is proposed for analyzing loads at the nodal level in order to detect whether a cyberattack has occurred in the node. The proposed system integrates computational intelligence with kernel modeled Gaussian processes and fuzzy logic. The overall goal of the intelligent system is to provide a degree of possibility as to whether the load demand is legitimate or it has been manipulated in a way that is a threat to the safety of the node and that of the grid in general. The proposed system is tested with real-world data.
Miltiadis Alamaniotis; Lefteri H. Tsoukalas. Learning from Loads: An Intelligent System for Decision Support in Identifying Nodal Load Disturbances of Cyber-Attacks in Smart Power Systems Using Gaussian Processes and Fuzzy Inference. Data Science Thinking 2017, 223 -241.
AMA StyleMiltiadis Alamaniotis, Lefteri H. Tsoukalas. Learning from Loads: An Intelligent System for Decision Support in Identifying Nodal Load Disturbances of Cyber-Attacks in Smart Power Systems Using Gaussian Processes and Fuzzy Inference. Data Science Thinking. 2017; ():223-241.
Chicago/Turabian StyleMiltiadis Alamaniotis; Lefteri H. Tsoukalas. 2017. "Learning from Loads: An Intelligent System for Decision Support in Identifying Nodal Load Disturbances of Cyber-Attacks in Smart Power Systems Using Gaussian Processes and Fuzzy Inference." Data Science Thinking , no. : 223-241.
The increasing demand for electricity the last decades leads towards the more frequent use of Combined Cycle Power Plants (CCPPs) because of the quite efficient way these units are capable to produce electricity. Hence, the prediction of the output of these units is of significant interest and constitutes the cornerstone towards the attainment of economic power production and a reliable power generation system as a whole. To that end, the aim of this paper is the development of a hierarchical predictive method based on Artificial Neural Networks (ANNs) in order to efficiently predict the power plant's output. The under consideration features are the hourly average ambient variables of Temperature (T), Ambient Pressure (AP), Relative Humidity (RH) and Exhaust Vacuum (V) for predicting the hourly power output of a CCPP. A parellel, but equally important, aim of this study is to assess the effectiveness of ANNs in this type of applications.
Rafik Fainti; Antonia Nasiakou; Miltiadis Alamaniotis; Lefteri H. Tsoukalas. Hierarchical Method Based on Artificial Neural Networks for Power Output Prediction of a Combined Cycle Power Plant. International Journal of Monitoring and Surveillance Technologies Research 2016, 4, 20 -32.
AMA StyleRafik Fainti, Antonia Nasiakou, Miltiadis Alamaniotis, Lefteri H. Tsoukalas. Hierarchical Method Based on Artificial Neural Networks for Power Output Prediction of a Combined Cycle Power Plant. International Journal of Monitoring and Surveillance Technologies Research. 2016; 4 (4):20-32.
Chicago/Turabian StyleRafik Fainti; Antonia Nasiakou; Miltiadis Alamaniotis; Lefteri H. Tsoukalas. 2016. "Hierarchical Method Based on Artificial Neural Networks for Power Output Prediction of a Combined Cycle Power Plant." International Journal of Monitoring and Surveillance Technologies Research 4, no. 4: 20-32.
Detection and identification of special nuclear materials (SNMs) are an essential part of the US nonproliferation effort. Modern cutting-edge SNM detection methodologies rely more and more on modeling and simulation techniques. Experiments with radiological samples in realistic configurations, is the ultimate tool that establishes the minimum detection limits of SNMs in a host of different geometries. Modern modeling and simulation approaches have the potential to significantly reduce the number of experiments with radioactive sources needed to determine these detection limits and reduce the financial barrier to SNM detection. Unreliable nuclear data is one of the principal causes of uncertainty in modeling and simulating nuclear systems. In particular, nuclear cross sections introduce a significant uncertainty in the nuclear data. The goal of this research is to develop a methodology that will autonomously extract the correct nuclear resonance characteristics of experimental data in a reliable way, a task previously left to expert judgement. Accurate nuclear data will in turn allow contemporary modeling and simulation to become far more reliable, de-escalating the extent of experimental testing. Consequently, modeling and simulation techniques reduce the use and distribution of radiological sources, while at the same time increase the reliability of the currently used methods for the detection and identification of SNMs.
Pola Lydia Lagari; Vladimir Sobes; Miltiadis Alamaniotis; Lefteri H. Tsoukalas. Application of Artificial Neural Networks to Reliable Nuclear Data for Nonproliferation Modeling and Simulation. International Journal of Monitoring and Surveillance Technologies Research 2016, 4, 54 -64.
AMA StylePola Lydia Lagari, Vladimir Sobes, Miltiadis Alamaniotis, Lefteri H. Tsoukalas. Application of Artificial Neural Networks to Reliable Nuclear Data for Nonproliferation Modeling and Simulation. International Journal of Monitoring and Surveillance Technologies Research. 2016; 4 (4):54-64.
Chicago/Turabian StylePola Lydia Lagari; Vladimir Sobes; Miltiadis Alamaniotis; Lefteri H. Tsoukalas. 2016. "Application of Artificial Neural Networks to Reliable Nuclear Data for Nonproliferation Modeling and Simulation." International Journal of Monitoring and Surveillance Technologies Research 4, no. 4: 54-64.
The modern way of living depends on a very high degree on electricity utilization. People take for granted that their energy needs will be satisfied 24/7 which mandates the maintaining of the power grid in stable state. To that end, the development of precise methods for monitoring and predicting events that might disturb its uninterrupted operation is immense. Moreover, the evolvement of power grids into smart grids where the end users continuously participate in the power market by forming energy prices and/or by adjusting their energy needs according to their own agenda, adds high volatility to load demand. In that sense, with regard to predictive methods, a plain single point prediction application may not be enough. The aim of this study is to develop and evaluate a method in order to further enhance this type of applications by providing Predictive Intervals (PIs) regarding ampacity overloading in smart power systems through the use of Artificial Neural Networks (ANNs).
Rafik Fainti; Miltiadis Alamaniotis; Lefteri H. Tsoukalas. Backpropagation Neural Network for Interval Prediction of Three-Phase Ampacity Level in Power Systems. International Journal of Monitoring and Surveillance Technologies Research 2016, 4, 1 -20.
AMA StyleRafik Fainti, Miltiadis Alamaniotis, Lefteri H. Tsoukalas. Backpropagation Neural Network for Interval Prediction of Three-Phase Ampacity Level in Power Systems. International Journal of Monitoring and Surveillance Technologies Research. 2016; 4 (3):1-20.
Chicago/Turabian StyleRafik Fainti; Miltiadis Alamaniotis; Lefteri H. Tsoukalas. 2016. "Backpropagation Neural Network for Interval Prediction of Three-Phase Ampacity Level in Power Systems." International Journal of Monitoring and Surveillance Technologies Research 4, no. 3: 1-20.
Energy production units are large complex installations comprised of several smaller units, subsystems, and mechanical components, whose monitoring and control to secure safe operation are high demanding tasks. In particular, human operators are required to monitor a high volume of incoming data and must make critical decisions in very short time. Although they are explicitly trained in such situations, there are cases that may not be able to identify a gradually developing crucial faulty state. To that end, automated systems can be used for monitoring operational quantities and detecting potential faults in time. The field of machine learning offers a variety of tools that may be used as the ground for developing automated monitoring and control systems for energy systems. In the current chapter, we present an approach that adopts a single Gaussian process learning machine in monitoring high complex energy systems. The Gaussian process is a data-driven model assigned to monitor a set of operational parameters. The values of the operational parameters at a specific instance comprise the system’s operational vector at that time instance. The operational vector consists the input to the individual Gaussian process machine whose task is to classify the operation of the system either as normal (or steady state) or match it to a faulty state. The presented approach is benchmarked on a set of experimentally data taken from the Fix-II test facility that is a representation of a Boiling Water Reactor. Obtained results exhibit the potential of Gaussian processes in monitoring highly complex systems such as nuclear reactors, by identifying with high accuracy the faults in system operation.
Miltiadis Alamaniotis; Stylianos Chatzidakis; Lefteri H. Tsoukalas. Data Driven Monitoring of Energy Systems: Gaussian Process Kernel Machine for Fault Identification with Application to Boiling Water Reactors. Econometrics for Financial Applications 2016, 177 -188.
AMA StyleMiltiadis Alamaniotis, Stylianos Chatzidakis, Lefteri H. Tsoukalas. Data Driven Monitoring of Energy Systems: Gaussian Process Kernel Machine for Fault Identification with Application to Boiling Water Reactors. Econometrics for Financial Applications. 2016; ():177-188.
Chicago/Turabian StyleMiltiadis Alamaniotis; Stylianos Chatzidakis; Lefteri H. Tsoukalas. 2016. "Data Driven Monitoring of Energy Systems: Gaussian Process Kernel Machine for Fault Identification with Application to Boiling Water Reactors." Econometrics for Financial Applications , no. : 177-188.
Integration of energy systems with information technologies has facilitated the realization of smart energy systems that utilize information to optimize system operation. To that end, crucial in optimizing energy system operation is the accurate, ahead-of-time forecasting of load demand. In particular, load forecasting allows planning of system expansion, and decision making for enhancing system safety and reliability. In this paper, the application of two types of kernel machines for medium term load forecasting (MTLF) is presented and their performance is recorded based on a set of historical electricity load demand data. The two kernel machine models and more specifically Gaussian process regression (GPR) and relevance vector regression (RVR) are utilized for making predictions over future load demand. Both models, i.e., GPR and RVR, are equipped with a Gaussian kernel and are tested on daily predictions for a 30-day-ahead horizon taken from the New England Area. Furthermore, their performance is compared to the ARMA(2,2) model with respect to mean average percentage error and squared correlation coefficient. Results demonstrate the superiority of RVR over the other forecasting models in performing MTLF.
Miltiadis Alamaniotis; Dimitrios Bargiotas; Lefteri H. Tsoukalas. Towards smart energy systems: application of kernel machine regression for medium term electricity load forecasting. SpringerPlus 2016, 5, 1 -15.
AMA StyleMiltiadis Alamaniotis, Dimitrios Bargiotas, Lefteri H. Tsoukalas. Towards smart energy systems: application of kernel machine regression for medium term electricity load forecasting. SpringerPlus. 2016; 5 (1):1-15.
Chicago/Turabian StyleMiltiadis Alamaniotis; Dimitrios Bargiotas; Lefteri H. Tsoukalas. 2016. "Towards smart energy systems: application of kernel machine regression for medium term electricity load forecasting." SpringerPlus 5, no. 1: 1-15.
The Journal of Pattern Recognition Research (JPRR) provides an open access forum for the publication of research articles in areas of pattern recognition, machine learning, artificial intelligence, computational algorithms, and fuzzy learning
Antonia Nasiakou; Miltiadis Alamaniotis; Lefteri Tsoukalas. Extending the K-Means Clustering Algorithm to Improve the Compactness of the Clusters. Journal of Pattern Recognition Research 2016, 11, 61 -73.
AMA StyleAntonia Nasiakou, Miltiadis Alamaniotis, Lefteri Tsoukalas. Extending the K-Means Clustering Algorithm to Improve the Compactness of the Clusters. Journal of Pattern Recognition Research. 2016; 11 (1):61-73.
Chicago/Turabian StyleAntonia Nasiakou; Miltiadis Alamaniotis; Lefteri Tsoukalas. 2016. "Extending the K-Means Clustering Algorithm to Improve the Compactness of the Clusters." Journal of Pattern Recognition Research 11, no. 1: 61-73.